How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring
Summary
This paper investigates how lightweight static analysis annotations can serve as deterministic anchors to improve the predictability and reproducibility of LLM-based code agents navigating software repositories, finding that such anchoring improves localization and reduces variance.
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Paper page - How Much Static Structure Do Code Agents Need? A Study of Deterministic Anchoring
Source: https://huggingface.co/papers/2606.26979
Abstract
Static analysis annotations improve code agent navigation by providing deterministic structural anchors that enhance predictability and reduce variability in software exploration.
LLM-based code agentsnavigate repositories throughkeyword searchbut miss the structural relationships, such ascall graphs,inheritance hierarchies, andconfiguration dependencies, that define how software actually works. This makes agent navigation stochastic and difficult to reproduce across runs. We investigate whether lightweightstatic analysiscan providedeterministic anchorsfor these agents: stable structural facts injected as plain-text comments that constrain probabilistic exploration and make navigation more predictable. Starting from a strong baseline, Codex from OpenAI, we systematically inject varying granularities ofstructural annotationsand measure their effects on localization, trajectory behavior, andrun-to-run stability. Our study identifies what we call the deterministic anchoring effect: static structure helps less by making agents “smarter” and more by making their navigation disciplined and reproducible. Three observations support this finding: (1) Anchoring works: lightweight call/inheritance topology improvesfunction-level localization(+2.2pp Func@5) and shortens trajectories (-1.6 interaction rounds); (2) Anchoring is scale-sensitive: the optimal granularity and directionality depend on repository characteristics, where denser semantics show diminishing returns and hub-heavy projects benefit from inverse-only links that expose “who-calls-me” without forward edges; (3) Anchoring stabilizes: tags raise link-following rate from 0.15-0.18 to 0.21-0.24, roughly halve run-to-run variance, and improve single-run reliability (Pass@1 +3.4 pp) on medium-scale repositories, at the cost of roughly 10% more input tokens. These observations suggest practical guidelines: default to lightweight topology on medium projects, prune forward edges in large repositories, and reserve dense tags for implicit-dependency cases.
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